“When big data intersects with highly sensitive data, both opportunity to society and risks abound. Traditional approaches for sharing sensitive data are known to be ineffective in protecting privacy. Differential Privacy, deriving from roots in cryptography, is a strong mathematical criterion for privacy preservation that also allows for rich statistical analysis of sensitive data. Differentially private algorithms are constructed by carefully introducing “random noise” into statistical analyses so as to obscure the effect of each individual data subject. OpenDP is an open-source project for the differential privacy community to develop general-purpose, vetted, usable, and scalable tools for differential privacy, which users can simply, robustly and confidently deploy.
Dataverse is an open source web application to share, preserve, cite, explore, and analyze research data. It facilitates making data available to others, and allows you to replicate others’ work more easily. Researchers, journals, data authors, publishers, data distributors, and affiliated institutions all receive academic credit and web visibility. A Dataverse repository is the software installation, which then hosts multiple virtual archives called Dataverses. Each dataverse contains datasets, and each dataset contains descriptive metadata and data files (including documentation and code that accompany the data).
This session examines ongoing efforts to realize a combined use case for these projects that will offer academic researchers privacy-preserving access to sensitive data. This would allow both novel secondary reuse and replication access to data that otherwise is commonly locked away in archives. The session will also explore the potential impact of this work outside the academic world.”